Fetch.ai, Ocean Protocol, and SingularityNET have finalized the details of their planned merger to form the Artificial Super Intelligence Alliance (ASI).
The new shared ASI token, which will have a market cap of $5.8 billion based on the tokens' respective prices as of May 28, represents the largest open-source, decentralized network focused on the AI industry.
The merger, which will be finalized on June 13, will rename FET to ASI and provide users with an Ethereum-based ERC-20 token to transfer their FET, AGIX, and OCEAN tokens to ASI in a secure, auditable manner. The token can be exchanged via a migration contract. This will allow more than 200,000 token holders of constituencies to deliver tokens to ASI.
Since the market capitalization of each project is different, different conversion rates are set. FET will be renamed ASI, with a total supply of 2.63055 billion tokens from FET and a 1:1 exchange rate. Meanwhile, AGIX tokens can be bridged to ASI at a rate of 0.433350:1, while OCEAN tokens will exchange at a rate of 0.433226:1.
Once the transition is complete, Artificial Super Intelligence Alliance could be the most important decentralized AI ecosystem in crypto with a wide range of use cases that appeal to large businesses to home users.
“This merger paves the way for a new era in AI, combining our strengths to make unprecedented progress,” Ben Goertzel, CEO of the Future ASI Alliance, said in a press release.
The fusion of SingularityNET, Fetch.ai, and Ocean Protocol's research, brands, technologies, and products underpins an open, scalable AI infrastructure that uses blockchain to ensure ethical and trustworthy practices in AI development and deployment. Takes advantage.
Humayun Shaikh, CEO and Co-Founder of Fetch.ai shared. Decrypt His vision on the driving force behind this ambitious integration—and how it could impact the AI development landscape:
“With this integration, we are reviewing three distinct product lines. The first is an agent network and accompanying tools that facilitate the development, connection and deployment of agents in commercial settings. Second, we are developing neural are focusing on the commercial deployment of symbolic LLMs and the framework that supports them, as well as facilitating communication between AI layers, either through agents or at the end, for the distribution and use of data. Looking for an independent solution.”
The partnership is designed to challenge Big Tech's dominance in AI development. The ASI Alliance aims to accelerate the commercialization and monetization of each foundation's technology and enable broad access to the latest AI platforms and vast data sets. Looking ahead, the ASI Alliance has set clear milestones for the next two to three years with three key objectives.
Shaikh shared that the first study involved heavy research “recognizing the need to explore and develop new AI, AGI, and ASI techniques to stay ahead of rapid obsolescence.” The second goal focuses on deploying heavy compute resources for the development and use of AI, and expanding its hardware infrastructure. And the ultimate goal is more predictable, focused on expanding the business model “by leveraging the synergy between data and compute, recognizing them as the twin pillars of our commercialization efforts.”
The ASI token will operate on a shared decentralized AI network, providing scale and power potentially unmatched by the Web3 network. Users wishing to use the shared power of the artificial superintelligence alliance would have to pay in tokens rather than going through a centralized entity, giving users the power to decide when to join or leave the network. Is.
“It involves two primary aspects: compute for training models and compute for inference. [We’re] Harnessing the combined power of these three elements for powerful commercial deployments, offering a synergy that has yet to be fully realised,” Sheikh explained. Decrypt.
This covers a lot of ground for the ASI team to explore. In AI, the training phase is a timed process where the model learns everything it needs to know, just like a student in a classroom. This is how AI businesses start. It requires a lot of computing power to process large amounts of data, make connections between them, and learn how to predict new results based on its knowledge base.
But after training the model on the dataset, it enters the inference phase where it uses its learned knowledge to analyze new, unseen data and generate inferences. Thus the end users benefit from the model. Therefore, the model learns during training and thinks during estimation. While AI training is a one-time process, inference is ongoing and involves real-time data processing, which can be expensive in terms of both computing power and energy consumption.
So “inference delivery” is end-user focused, while “compute for training models” is a business model in which ASI provides the infrastructure that other developers use to build their own AI products.
The leaders of Fetch.ai, Ocean Protocol, and SingularityNET appear eager to officially launch ASI and welcome additional strategic partners to the alliance in the near future.
“The ASI Alliance is going to be a game changer for Web3 adoption for AI and data,” said Bruce Poon, founder of Ocean Protocol and director of the ASI Council Board, in a statement. “We have worked out a lot of details to make this process go smoothly, and we look forward to officially launching ASI.”
Edited by Andrew Hayward